skypilot README

The project's README from github.com/skypilot-org/skypilot. As one markdown file: README.md.

SkyPilot

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Manage all your AI compute

SkyPilot is a system to run, manage, and scale AI workloads on any AI infrastructure.

SkyPilot gives AI teams a simple interface to run jobs on any infra. Infra teams get a unified control plane to manage any AI compute — with advanced scheduling, scaling, and orchestration.

SkyPilot Abstractions

:fire: News :fire:

Overview

SkyPilot is easy to use for AI users:

SkyPilot makes Kubernetes easy for AI & Infra teams:

SkyPilot unifies multiple clusters, clouds, and hardware:

SkyPilot maximizes GPU fleet utilization:

SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.

Install with uv (also supported: pip, nightly, from source)

# Choose your clouds:
uv pip install "skypilot[kubernetes,aws,gcp,azure,oci,nebius,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,seeweb,shadeform,verda]"

To use SkyPilot directly with your agent (Claude Code, Codex, etc.), install the SkyPilot Skill. Tell your agent:

Fetch and follow https://github.com/skypilot-org/skypilot/blob/HEAD/agent/INSTALL.md to install the skypilot skill

SkyPilot

Current supported infra: Kubernetes, Slurm, AWS, GCP, Azure, OCI, CoreWeave, Nebius, Lambda Cloud, RunPod, Fluidstack, Cudo, Digital Ocean, Paperspace, Cloudflare, Samsung, IBM, Vast.ai, VMware vSphere, Seeweb, Prime Intellect, Shadeform, Verda Cloud, VastData, Crusoe.

SkyPilot

Getting started

Install SkyPilot in 1 minute. Then, launch your first cluster in 2 minutes in Quickstart.

SkyPilot is BYOC: Everything is launched within your cloud accounts, VPCs, and clusters.

Benefits of SkyPilot on Kubernetes

SkyPilot makes Kubernetes AI-native.

It turbocharges your existing Kubernetes clusters by accelerating AI/ML velocity:

...and optimizing GPU scheduling, utilization, and scaling:

See SkyPilot vs Vanilla Kubernetes and this blog post for more details.

SkyPilot in 1 minute

A SkyPilot task specifies: resource requirements, data to be synced, setup commands, and the task commands.

Once written in this unified interface (YAML or Python API), the task can be launched on any available infra (Kubernetes, Slurm, cloud, etc.). This avoids vendor lock-in, and allows easily moving jobs to a different provider.

Paste the following into a file my_task.yaml:

resources:
  accelerators: A100:8  # 8x NVIDIA A100 GPU

num_nodes: 1  # Number of VMs to launch

# Working directory (optional) containing the project codebase.
# Its contents are synced to ~/sky_workdir/ on the cluster.
workdir: ~/torch_examples

# Commands to be run before executing the job.
# Typical use: pip install -r requirements.txt, git clone, etc.
setup: |
  cd mnist
  pip install -r requirements.txt

# Commands to run as a job.
# Typical use: launch the main program.
run: |
  cd mnist
  python main.py --epochs 1

Prepare the workdir by cloning:

git clone https://github.com/pytorch/examples.git ~/torch_examples

Launch with sky launch (note: access to GPU instances is needed for this example):

sky launch my_task.yaml

SkyPilot then performs the heavy-lifting for you, including:

  1. Find the cheapest & available infra across your clusters or clouds
  2. Provision the GPUs (pods or VMs), with auto-failover if the infra returned capacity errors
  3. Sync your local workdir to the provisioned cluster
  4. Auto-install dependencies by running the task's setup commands
  5. Run the task's run commands, and stream logs

See Quickstart to get started with SkyPilot.

Runnable examples

See SkyPilot examples that cover: development, training, serving, LLM models, AI apps, and common frameworks.

Latest featured examples:

Task Examples
Training Verl, Finetune Llama 4, TorchTitan, PyTorch, DeepSpeed, NeMo, Ray, Unsloth, Jax/TPU, OpenRLHF
Serving vLLM, SGLang, Ollama
Models DeepSeek-R1, Llama 4, Llama 3, CodeLlama, Qwen, Kimi-K2, Kimi-K2-Thinking, Mixtral
AI apps RAG, vector databases (ChromaDB, CLIP)
Common frameworks Airflow, Jupyter, marimo

Source files can be found in llm/ and examples/.

Learn more

To learn more, see SkyPilot Overview, SkyPilot docs, and SkyPilot blog.

SkyPilot adopters: Testimonials and Case Studies

Follow updates:

Questions and feedback

We are excited to hear your feedback:

For general discussions, join us on the SkyPilot Slack.

Contributing

We welcome all contributions to the project! See CONTRIBUTING for how to get involved.

skypilot (skypilot-org/skypilot)

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这是做什么用的?SkyPilot 是一个开源系统,用于在任何基础设施上运行、管理和扩展 AI 工作负载——包括训练、推理服务和批量推理:可以是自己的 Kubernetes 或 Slurm 集群、包下来的 GPU,也可以是 20 多家云服务商。
那它是一个我要 import 到训练代码里的库,还是一个直接运行的工具?是一个工具,不是库:用 pip 或 uv 安装后,用 YAML 或 Python API 描述任务,然后在终端里提交。现有的 GPU、TPU 和 CPU 工作负载无需改代码就能直接运行。
我怎么提交第一个任务?先用 uv 或 pip 安装,写一个任务 YAML(写明资源需求、setup 命令和 run 命令),然后运行 'sky launch'。它会自动找到便宜且可用的基础设施,完成资源开通,同步你的工作目录,安装依赖,并实时输出日志。
它实际能用哪些云和集群?支持 Kubernetes、Slurm、AWS、GCP、Azure、OCI 以及 20 多家其他服务商,包括 CoreWeave、Nebius、Lambda Cloud、RunPod、Digital Ocean、Paperspace、IBM 和 VMware vSphere——全部通过同一个接口使用。
如果我常用的云上没 GPU 了怎么办?它可以灵活地在 GPU、TPU 和 CPU 之间开通资源,并带有智能故障转移:如果某家云报了资源不足的错误,任务会自动转到其他可用的基础设施上,而不是直接失败。
它是给谁用的——跑实验的研究人员,还是平台团队?两者都是:AI 团队可以通过简单的接口在任何基础设施上跑任务;基础设施团队则能获得统一的控制平面,对所有算力进行高级调度、扩展和编排。
有 SkyPilot 自己的云吗,还是跑在我自己的账号里?没有 SkyPilot 云:它采用 BYOC 模式,一切都在你自己的云账号、VPC 和集群里启动。工具本身免费开源(Apache-2.0),云费用直接付给服务商。
我的 GPU 有一半时间是闲置的——它能解决这个问题吗?能:Autostop 会自动清理闲置资源,工作负载会在共享集群上做装箱调度,智能调度器还会把任务安排到最空闲的基础设施上,最大化整体资源利用率。
开箱就能跑哪些东西?现成的示例涵盖训练(Verl、TorchTitan、DeepSpeed、Unsloth、OpenRLHF、TPU 上的 Jax)、推理服务(vLLM、SGLang、Ollama)、热门模型(DeepSeek-R1、Llama 4、Qwen、Kimi-K2)、RAG 和向量数据库这类 AI 应用,还有 Airflow 和 Jupyter 等框架。
我已经在用 Kubernetes 了——SkyPilot 还能带来什么?一层面向 AI 的封装:不用再折腾各种 manifests 就能启动任务和部署,可以 SSH 进 pod、同步代码、连接你的 IDE,还提供 gang 调度、排队,以及在同一个控制平面下管理多集群和多云。
我可以让我的编程智能体来操作它吗?可以——有一个面向 Claude Code、Codex 等智能体的 SkyPilot Skill,你的智能体可以直接安装并驱动集群。
SkyPilot Endpoints 和 Sandboxes 是什么?Endpoints(2026 年 6 月推出)在你拥有的每个集群上提供生产级推理服务。Sandboxes 则在你现有的 Kubernetes 集群上运行不可信的 LLM 生成代码,目前处于抢先体验阶段。
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